feat(litellm): wire SearXNG search, pgvector knowledgebase, and memory ingestion

- search_tools block in litellm-config.yaml (SearXNG as a first-class
  search_provider, standalone /v1/search endpoint, not a model tool) plus
  extra_hosts on the litellm service so it can resolve search.home.
- New embedding-server (nomic-embed-text-v1.5 on a second llama.cpp
  instance), pgvector-db, and litellm-pgvector services — LiteLLM's native
  knowledgebase feature has no Qdrant backend, so this is the only
  self-hosted path (docs/research/litellm-knowledgebase.md).
- vector_store_registry + local-embedding model entry in
  litellm-config.yaml, wiring it together.
- scripts/ingest-memory.sh to load data/memory.md and
  data/claude-legacy-memory.md into the knowledgebase.
- docs/memory-knowledgebase.md documenting the whole setup; data/
  gitignored (personal memory content, not meant to be committed).
- New .env vars (SEARXNG_LAN_IP, PGVECTOR_DB_PASSWORD,
  LITELLM_PGVECTOR_API_KEY, LITELLM_PGVECTOR_EMBEDDING_KEY,
  EMBEDDING_MODEL_FILE) and generate-secrets.sh support for the
  auto-generatable ones.

Resolves #22 and #23 (wayfinder map #21). Not yet verified on real
hardware — see #24.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

litellm-config.yaml

@@ -22,6 +22,44 @@ model_list:
       input_cost_per_token: 0.000002   # $2 / MTok
       output_cost_per_token: 0.00001   # $10 / MTok
 
+  - model_name: local-embedding
+    litellm_params:
+      # Served by the dedicated embedding-server (nomic-embed-text-v1.5), not
+      # the chat model — see docker-compose.yml. Called by litellm-pgvector
+      # to embed knowledgebase content, and available directly at
+      # /v1/embeddings for anything else that wants it.
+      model: openai/local-embedding
+      api_base: http://embedding-server:8080/v1
+      api_key: local
+    model_info:
+      mode: embedding
+
+# SearXNG-backed web search — a standalone REST endpoint (/v1/search/searxng-search),
+# NOT a model-callable tool and not auto-injected into chat completions. See
+# docs/research/litellm-searxng-search.md. Requires the litellm container to
+# resolve search.home — see the `extra_hosts` entry in docker-compose.yml.
+search_tools:
+  - search_tool_name: searxng-search
+    litellm_params:
+      search_provider: searxng
+      api_base: http://search.home/
+
+# Knowledgebase / RAG, backed by the litellm-pgvector companion service (NOT
+# Qdrant — LiteLLM's native vector-store feature has no Qdrant provider, see
+# docs/research/litellm-knowledgebase.md). vector_store_id is this proxy's
+# own identifier for the store, not assigned by a backend.
+# ponytail: field names here (custom_llm_provider: pg_vector, api_base
+# pointed at litellm-pgvector) are the best fit from the litellm-pgvector
+# README, not confirmed against a running deploy yet — smoke-test before
+# relying on it. See issue #24.
+vector_store_registry:
+  - vector_store_name: memory-and-notes
+    litellm_params:
+      vector_store_id: "memory-and-notes"
+      custom_llm_provider: pg_vector
+      api_base: http://litellm-pgvector:8000
+      embedding_model: local-embedding
+
 router_settings:
   # ponytail: LiteLLM's request-prioritization scheduler is beta (see
   # docs/proxy-request-priority.md) — exact settings key/shape must be